Algorithmic Examples of Runtime Analysis
Below table categorizes algorithms based on their runtime complexity and provides examples for each type.
Type | Notation | Example Algorithms |
---|---|---|
Logarithmic | O(log n) | Binary Search |
Linear | O(n) | Linear Search |
Superlinear | O(n log n) | Heap Sort, Merge Sort |
Polynomial | O(n^c) | Strassenâs Matrix Multiplication, Bubble Sort, Selection Sort, Insertion Sort, Bucket Sort |
Exponential | O(c^n) | Tower of Hanoi |
Factorial | O(n!) | Determinant Expansion by Minors, Brute force Search algorithm for Traveling Salesman Problem |
Big O Notation Tutorial â A Guide to Big O Analysis
Big O notation is a powerful tool used in computer science to describe the time complexity or space complexity of algorithms. It provides a standardized way to compare the efficiency of different algorithms in terms of their worst-case performance. Understanding Big O notation is essential for analyzing and designing efficient algorithms.
In this tutorial, we will cover the basics of Big O notation, its significance, and how to analyze the complexity of algorithms using Big O.
Table of Content
- What is Big-O Notation?
- Definition of Big-O Notation:
- Why is Big O Notation Important?
- Properties of Big O Notation
- Common Big-O Notations
- How to Determine Big O Notation?
- Mathematical Examples of Runtime Analysis
- Algorithmic Examples of Runtime Analysis
- Algorithm Classes with Number of Operations and Execution Time
- Comparison of Big O Notation, Big Ί (Omega) Notation, and Big θ (Theta) Notation
- Frequently Asked Questions about Big O Notation
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